Presentation by: Carlos Pinheiro & Natalia Summerville, SAS Institute
November 17, 2020
Abstract:
The recent outbreak of the coronavirus disease 2019 (COVID-19) has infected millions and killed several thousands of people globally, resulting in a pandemic with enormous global impact. As an extremely transmittable viral infection, COVID-19 is causing significant damage to the economies around the globe due to direct impact on the health of citizens and the measures taken to contain the infection that lead to economic damage. Methods to reduce or control the spread of the virus and protect the global population are mandatory to avoid further deaths, long-term health issues and prolonged economic impact.
The most effective approach to reduce viral spread and to avoid a substantial collapse of the health system is non-pharmaceutical interventions such as social containment, monitoring of overall population mobility, implementation of widespread viral testing and increase of hygienic measures. Our approach consists of combining network analysis with machine learning models using a combination of health and anonymized telecommunications data to better understand the correlation between population movements and virus spread.
This approach – called location network analysis – allows for accurate prediction of new possible outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health measures and can be used to either ease social containment policies to reopen the economy or to intensify them to avoid further spread.
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